YAKOV KESELMAN

425.638.3968

Summary

Experienced, highly creative developer of algorithms and software is looking for a fast-paced, collaborative environment that will utilize his excellent problem solving skills and expertise in applied data analysis (machine learning, data mining, statistical data analysis) and discrete optimization (graph algorithms, linear/dynamic programming).

Relevant Experience

September 2008 – present: Staff Software Engineer, Display Ads Team, Disney Interactive Media Group, Seattle, WA.

· Designed and implemented in SQL Server Analysis Services a small data warehouse in support of revenue optimization from video ads.

· Designed and implemented in SQL/MDX/Excel accuracy evaluation frameworks for website traffic forecasting and for target ratio forecasting, for use by business units.

· Developed and implemented in Python/SQL a prototype of improved graph-based inventory assignment techniques that resulted in additional contract opportunities.

· Obtained improved estimates of contract booking rates using data mining tools, resulting in reduction in contract rejection rates due to insufficient inventory.

January 2008 – August 2008: Software Development Engineer, Item Data Management, Amazon.com, Seattle, WA.

Designed and implemented in Python/SQL a rule-based evaluator of data reconciliation strategies (deriving an optimal value for an attribute from multiple attribute values).
Adapted existing contribution matching tools to the duplicate ASIN detection problem.

September 2006 – December 2007: Software Development Engineer, SQL Server Analysis Services, Microsoft Corporation, Redmond, WA.

Designed, implemented in C++, tested, and documented improved MDX query processing strategies for the next release of Microsoft SQL Server Analysis Services.
Debugged and fixed several customer-reported issues related to MDX query processing.

September 2005 – August 2006: Independent researcher specializing in applications of machine learning and optimization techniques to practical problems in data analysis.

Designed and implemented in Java a subsystem aimed at integration of multiple sources of geographical data. The subsystem was used in a larger system that extracts useful geographical patterns from large repositories of satellite imagery (NCSA, NASA).
Addressed the problem of computing an optimal representative of a cluster of graphs by a combination of several graph-based approximation algorithms. Implemented the approach in C++/LEDA. Validated the approach on real data.

September 2001 – July 2005: Assistant Professor, School of Computer Science, Telecommunications and Information Systems, DePaul University, Chicago, IL.

Reformulated the problem of image matching as a many-to-many matching problem on attributed graphs. To address the latter problem, adapted existing graph optimization techniques and co-developed novel approximation graph algorithms. Co-implemented the algorithms in C++/LEDA. Validated the approach on real data. Results were published in several leading pattern recognition conferences and journals.
Supervised a Ph.D. student in designing and implementing evolutionary (genetic) algorithms aimed at finding an optimal artificial neural network architecture for edge detection in images. Results were presented at a student conference.
Through teaching and research, developed thorough understanding of practical data management techniques, including: indexing and retrieval of textual and image data, system performance optimization via distribution and multithreading, data warehousing, data cleansing, data classification and clustering, data-driven decision making.

September 1994 – May 2001: Research and Teaching Assistant, Department of Computer Science, Rutgers University, Piscataway, NJ.

Reformulated the problem of image-based automated model acquisition for generic object recognition as an optimization problem on attributed graphs. Developed approximation algorithms for solving the formulation. Implemented the algorithms in C++/LEDA. Results were published in a leading pattern recognition journal.
Lead a team of 4 graduate and undergraduate students in the design and development of a distributed multithreaded system for robot tracking and control that is robust to noise and changes in the environment. Implemented system’s GUI in Tcl/Tk and C++.
Designed and implemented in Matlab a system for extraction and characterization of regions of interest in biomedical images. Validated the system on actual data. Presented an overview of the system at a leading conference in biomedical engineering.
Implemented in C a distributed version of an optimization algorithm for a linear programming problem. Its results were used in a journal publication.

List of Publications: http://www.yashma.org/yakovkeselman/Publications/

Education

· Ph.D., Computer Science, Rutgers University, Piscataway, NJ. 3.8 GPA. May, 2005.

o Coursework included artificial intelligence, robust statistical estimation, databases, design and analysis of algorithms, linear programming, randomized algorithms, numerical analysis, network flows, image processing, and computer vision.

o Received Rutgers University fellowships in Digital Libraries and in Cognitive Science.

· M.A., Mathematics, University of Georgia, Athens, GA. 3.8 GPA. June, 1994.

· B.S., Mathematics and Computer Science, the Urals State University, Ekaterinburg, Russia. 3.8 GPA. June, 1991.